Business organizations retain electronic documents, records and other data in storage for extended periods of time for a number of reasons including easy access, internal policy, and regulation compliance, among other various reasons. For instance, government regulation may require an organization to retain certain securities information for a given duration for SEC compliance. Likewise, some organizations retain electronic records of documents for audit and/or litigation purposes.
Oftentimes data storage systems involve storing data with an associated retention policy. The retention policy indicates a time period for the retention of the data and when the time period lapse, the data is typically disposed of automatically. According to known methods, a system administrator must dispose of data manually based on retention policies stored on paper or in other non structured form. Organizations managing a large amount of stored data will incur time-consuming and costly expenses in performing data disposition manually.
Furthermore, an error in data disposition may result in dire consequences. For instance, in cases where data wasn't disposed of, too much data has been disposed of, or wrong data has been disposed of, an organization may incur unwanted legal and business consequences. Therefore, there is a need in eliminating human factor from data disposition as much as possible.
There is no uniform view on how to manage disposition of data. This needs to be changed in order to get under control growing storage and legal costs associated with storing unnecessary information. Vendors should start designing their applications with data disposition in mind. To achieve that, they need to agree on common ways to manage data disposition.
Different types of data is associated with different retention schedules (i.e. rules describing how long the information should be preserved in a certain data source, what is the type of event that triggers measuring of the disposition period, and what should be done with the information when the disposition period is due). Some data sources can enforce retention schedules e.g. dispose of data using the rules defined by retention schedules, some cannot. For the latter type, it is hard to build an automated disposition solution using enterprise integration technologies such as integration middleware. This is mainly because retention schedule information is missing in most data sources and because the retention schedule information is a critical part of a solution that defines delays between the time a triggering event occurred and the time when a document should be disposed of.
Enterprise level data retention management is an emerging technology. Currently, Enterprise Retention Management systems (ERM systems) store references to data sources and store retention policies in a structured format. However, the retention policies were not utilized in a structured format to affect the disposition of data.